Multisensor Fusion Time–Frequency Analysis of Thruster Blade Fault Diagnosis Based on Deep Learning

With the rapid development of marine robots, detecting abnormalities in propulsion systems is important during sailing as the unperceived damage of thrusters and propellers can cause substantial losses. In this study, different fault conditions of blades, including healthy, fully broken, half-broken, and simulated biofouling, are discussed. Current and sound signals are collected by a Hall element and hydrophone, respectively, to diagnose the propeller under different rotating speeds. The experiments include an ideal condition (swimming pool) and a noisy condition (lake). The raw data of time-domain signals are transformed into a time–frequency domain and shown as an image. A modified convolutional neural network (CNN) based on merging two signals is proposed to classify the faults. To compare the performance of models, the networks use single and multiple signals as input. The results demonstrate that the proposed multiple signals method achieves the best propeller fault diagnosis results, particularly when two signals are first trained separately and then merge at the end (99.94% in a swimming pool and 99.06% in a lake). Finally, the model was applied to Nvidia Jetson TX2 to verify the computing performance of an embedded system.